AI-Enhanced Medical Imaging to Improve Radiology Workflows

March 21, 2018

As the amount of medical imaging data continues to increase, so does the workload of radiologists. Radiologists are being asked to participate more in patient care, while handling more and more imaging studies. What if AI technology could help radiologists get their jobs done faster and more accurately?

Deep learning and other forms of artificial intelligence (AI) have the potential to streamline medical imaging workflows, improve image acquisition techniques to reduce X-ray exposure, and increase the research value of image data. We’re seeing promising demonstrations of high-performance medical imaging AI and deep learning running on the same general-purpose Intel® architecture that customers use for so many other workloads.

Practical Deep Learning in Medical Imaging Workflows

Medical images can be valuable for diagnosing illness, planning treatment, and assessing results. Aggregated with other health and demographic data, they can generate novel insights and inspire breakthrough treatments. For example:

Deep learning based image techniques are being used to populate metadata associated with images or to position patients. This metadata can be used to dynamically adapt radiology workflows and make image retrieval faster, with the added benefit of ensuring accuracy.

However, deep learning medical imaging workflows are not without challenge. The deep learning inference needs to be extremely fast, on the order of milliseconds per image slice, so that the radiologist’s workflow is not adversely impacted. The deployment cost of AI compute needs to be low. The cost of add-in cards such as GPUs can quickly multiply, and can also result in added operational costs due to additional maintenance requirements. The platform needs to be supported for several years to minimize regulatory recertification.

Deep Learning Inference on Common Infrastructure

As deep learning becomes pervasive, customers are looking for a flexible architecture that allows deep learning inference in the datacenter or cloud. Additionally, customers have found that infrastructure needs for deep learning training differ significantly from those for deep learning inference. A different inference infrastructure can greatly increase inference throughput.

Intel® Xeon® Scalable processors offer great capabilities for AI inference and are found in almost every datacenter deployment. Due to this, it is increasingly attractive to deploy deep learning inference on Intel hardware when possible.

Software optimizations can often drive additional performance in deep learning solutions on Intel architecture. The Intel DL Deployment Toolkit, a recent addition to the Intel Computer Vision SDK, allows users to optimize deep learning models for faster inference execution on Intel architecture. It can import trained models from Caffe*, TensorFlow*, and other popular deep learning frameworks—regardless of the hardware platform used to train the model.

Putting Intel Inferencing to the Test

Customers like GE Healthcare, a leading provider of medical imaging equipment, have achieved impressive results using Intel® Xeon® Processors and the Intel DL Deployment Toolkit for deep learning medical imaging solutions. A team from GE Healthcare’s Computed Tomography (CT) scan division recently used Intel DL Deployment Toolkit in a solution that classifies and tags scanned image slices in order to make them easier to find and use in research or clinical comparison.

After developing their model using Python* and open source software including TensorFlow* and Keras* libraries for deep learning, GE’s CT image specialists teamed with Intel to use the Intel DL Deployment Toolkit to set up and optimize the solution for Intel architecture. Reflecting the need for cost-effective performance, GE targeted throughput of 100 images per second using no more than four dedicated cores of an Intel Xeon processor.

The team’s results far exceeded this goal. In fact, a single core of the Intel® Xeon® processor E5-2650 v4 demonstrated throughput nearly 150% greater than GE’s overall performance target. Powering the solution with four of the processor’s cores exceeded GE’s throughput goal by almost 6x. We expect that the new Intel® Xeon® Platinum 8180 processor, which deliver up to 2.4x higher performance for a range of AI workloads compared to the previous generation, would yield even greater throughput.

“We want to keep deployment costs down for our customers, so we need the performance and flexibility to run a whole range of AI and imaging processing tasks in a variety of clinical environments,” said David Chevalier, Principal Engineer, GE Healthcare. “We think using general-purpose processors, tools, and frameworks from Intel can offer a cost-effective way to leverage AI in medical imaging in new and meaningful ways.”

Inference on Common Infrastructure

Using the Intel DL Deployment Toolkit, innovators like GE Healthcare can achieve excellent deep learning inference performance on the same cost-effective, general-purpose infrastructure relied upon for other image-processing tasks. Intel technologies are helping GE Healthcare and companies like it deliver on AI’s potential to improve countless lives worldwide.

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